Imaging, Diagnosis, Prognosis

Primary Tumor Levels of Human Tissue Affect Surgical Success and Survival in Ovarian Cancer Patients Julia Dorn,1Manfred Schmitt,1Ronald Kates,1Barbara Schmalfeldt,1Marion Kiechle,1 Andreas Scorilas,2 Eleftherios P. Diamandis,3,4,5 and Nadia Harbeck1

Abstract Purpose: Proteolytic factors of the human tissue (hK) family and the plasminogen activation system play a key role in tumor progression in various malignancies. We determined antigenlevels of -type (uPA), its inhibitor PAI-1,and hK5-8, hK10, hK11, and hK13 by ELISA in primary tumor tissue extracts of 142 International Federation of Gynecology and Obstetrics (FIGO) I to IVovarian cancer patients (median follow-up 41months). Results: After radical surgery, absence of macroscopically visible residual tumor (RT) was achieved in 72 patients; all patients received postoperative platinum-containing chemotherapy. Significant univariate predictors of poor progression-free survival (PFS) were RT (>0), FIGO stages (III/IV versus I/II/III), ascites volume >500 mL, nodal status, and the difference between PAI-1and uPA (fractionally ranked). In multivariate analysis, significant independent factors for poor PFS were RT [hazard ratio (HR), 4.53] and low hK11fractional rank (HR, 0.30). Univariate predictors of poor overall survival were RT, FIGO stages, nodal status, ascites volume, nuclear grade, and low hK10 and hK13. In multivariate analysis, significant independent factors for poor overall survival were RT (HR, 7.49), ascites (HR, 1.97), and low hK10 (HR, 0.196).We constructed a multivariate scoring model estimating RT probability, based on ascites [odds ratio (OR), 13.1], nuclear grade (OR, 2.92), hK6 (OR, 8.54), and hK13 (OR, 0.14), with good in-sample predictive performance (area under receiver operating characteristic, 0.833). Conclusions: In view of risks and benefits of radical surgery, such a score could support pre- operative risk stratification and identify candidates for alternative therapeutic strategies. These results highlight the distinct roles of the hKs for different disease end points in ovarian cancer and their potential to support individualized therapy decisions.

Epithelial ovarian cancer, with the highest mortality rate of and two thirds eventually suffer recurrence and death anyway. all gynecologic malignancies, is seldom diagnosed before the Incorporating tumor-associated biomarkers into selection of tumor is disseminated throughout the entire abdominal cavity. candidates for novel primary clinical therapy approaches State-of-the-art therapy consists of radical debulking surgery could improve survival or at least reduce needless morbidity. with the goal of complete tumor resection, followed by Conceivable options enabled by better primary scoring might combination chemotherapy. However, therapeutic interven- include preoperative (neoadjuvant) chemotherapy or a tions are associated with considerable morbidity. In patients second-effort surgical approach after incomplete primary with advanced disease, radical surgery leads to f3% mortality, surgery. Numerous studies have focused on improved understanding of the underlying tumor biology in ovarian cancer. Tumor proteases, especially urokinase-type plasminogen activator Authors’ Affiliations: 1Department of Obstetrics and Gynecology, Klinikum rechts (uPA), a , and its inhibitor PAI-1, may play a der Isar, Technical University of Munich, Munich, Germany; 2NCS Research key role in invasion and metastasis, because both uPA and PAI- Demokritos, National University of Athens, Athens, Greece; 3Department of 1 antigen levels are elevated in ovarian cancer tissue compared 4 Pathology and Laboratory Medicine, Mount Sinai Hospital; Department of Clinical with benign ovarian tumors (1–3). Moreover, uPA and PAI-1 Biochemistry, University Health Network and Toronto Medical Laboratories; and 5Department of Laboratory Medicine and Pathobiology, University of Toronto, are strong prognostic markers in advanced ovarian cancer Toronto, Ontario, Canada (2, 4), especially in patients without residual tumor (RT) mass. Received 10/13/06; revised 12/1/06; accepted 12/18/06. Evidence for involvement of the tissue kallikrein serine Grant support:Wilhelm Sander Foundation grant 2001.009.2 (N. Harbeck). protease family in ovarian cancer spread is emerging. The tissue The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance kallikrein family genes are clustered (5) on chromosome with 18 U.S.C. Section 1734solely to indicate this fact. 19q13.4. Thus far, 15 human tissue kallikrein (hK) protease Requests for reprints: Nadia Harbeck, Departmentof Obstetrics and Gynecology, proteins have been identified. In ovarian cancer tissue, hK4-8, Klinikum rechts der Isar,Technical University of Munich, Ismaninger Strasse 22, hK10, hK11, hK13, and hK15 are differentially expressed at D-81675 Munich, Germany. Phone: 49-89-4140-6658; Fax: 49-89-4140-4846; mRNA and protein levels (6). Some tissue kallikrein proteins E-mail: [email protected]. F 2007 American Association for Cancer Research. (hK5, hK6, hK8, hK10, and hK11) seem to be useful serum doi:10.1158/1078-0432.CCR-06-2482 diagnostic markers in ovarian cancer with complementary

Clin Cancer Res 2007;13(6) March15, 2007 1742 www.aacrjournals.org Downloaded from clincancerres.aacrjournals.org on September 28, 2021. © 2007 American Association for Cancer Research. Kallikreins Affect Surgical Success and Survival value to that of the tumor marker CA125 (7). Serum hK6 After gentle rotation for 15 h at 4jC, followed by ultracentrifugation  protein levels decrease in 68% of patients after surgery (8); for 45 min at 100,000 g (4jC), the supernatant (extract) was A hK10 is elevated in serum of ovarian cancer patients, but not collected, aliquoted (50 L portions), and stored in liquid nitrogen until further use. in patients with benign gynecologic diseases. High hK10 in Determination of antigen levels of uPA, PAI-1, and tissue kallikreins in serum has been associated with late-stage, advanced-grade, ovarian cancer tissue extracts. Protein concentrations of the extracted suboptimal tumor debulking as well as shorter progression-free tissue specimens were determined using the BCA Protein Assay reagent survival (PFS) and overall survival (OS; ref. 9). In ovarian kit (Pierce, Rockford, IL). Antigen levels of the proteolytic factors uPA, cancer tissue extracts, hK5, hK6, hK7, and hK10 have been its inhibitor PAI-1, and seven tissue kallikreins (hK5, hK6, hK7, hK8, reported as markers of poor prognosis: hK5 is significantly hK10, hK11, hK13) were determined by ELISA in primary ovarian elevated in ovarian cancer cytosols, compared with low- cancer tissue extracts. uPA and PAI-1 antigen levels were measured using malignant-potential tumors. Higher hK5 levels are associated ELISA kit Imubind #894 and #821 (American Diagnostica, Inc., with advanced disease and significantly shorter PFS and OS Stamford, CT), as described previously (16). Antigen concentrations of hK proteins were quantified by using highly sensitive and specific (10), whereas high levels of hK8, hK11, and hK13 are asso- sandwich-type, in-house immunoassays (17). Capture antibodies were ciated with less aggressive and less advanced disease (5, 11). generated by immunizing mice with recombinant hK protein (mono- hK11-positive tumors were associated with early stage and clonal for hK5, hK11, and hK13; polyclonal for hK6, hK7, hK8, and low tumor grade; high tumor levels of hK11 and hK13 imply hK10), detection antibodies by immunizing rabbits (all polyclonal). longer PFS and OS (12, 13). Besides their prognostic effect, Lower and upper detection limits were 0.1 to 50 ng/mL for hK5 (18), elevated hK10 and hK13 have been associated with optimal 0.5 to 200 ng/mL for hK6 (19), 0.2 to 10 ng/mL for hK7 (20), 0.2 debulking (9, 13), and elevated hK11 has been associated with to 20 ng/mL for hK8 (21), 0.05 to 10 ng/mL for hK10 (22), 0.1 to response to chemotherapy (14). 50 ng/mL for hK11 (12), and 0.05 to 20 ng/mL for hK13 (23). No cross-reactivity of any of the hK-ELISAs with other members of the Despite this emerging evidence for their role in disease hK family was detected. Analyte levels measured in the extracts were processes, there is no clear basis yet for incorporating these expressed in nanograms of analyte per milligram of protein. proteolytic factors into evidence-based decision criteria in Statistical methods. Outcome variables were PFS, OS, and RT ovarian cancer. Key steps are to assess their relative and presence, defined as one if macroscopic RT mass was visible and zero combined effects on surgical success and survival in a if completely absent. Ascites volume, age, nuclear grade, and nodal homogeneous, representative patient cohort, to determine status were coded as binary variables (ascites >500 mL versus less; age which factors are essential for improving the quality of existing >60 years versus younger; nuclear grade, G3 versus G1/G2; nodal status models, and to construct multivariate models suitable for 0 for N0, otherwise 1). FIGO status was coded by three binary future validation and clinical decision support. This article indicators: II/III/IV versus I, III/IV versus I/II, and IV versus I/II/III. evaluates the clinical effect of seven tissue kallikreins (hK5, hK6, hK7, hK8, hK10, hK11, and hK13) as well as that of uPA and PAI-1 in extracts of tumor tissue samples obtained from primary ovarian cancer patients; the results are translated Table 1. Clinical and histomorphologic into clinically applicable, multivariate scoring models for characteristics of the cohort surgical success and survival, including both established clinical variables and proteolytic factors. The goal of combining all this n (%) information is to support better clinical decisions and FIGO stage ultimately to improve both survival and quality of life for I 25 (17.6) patients. II 10 (7.0) III 78 (54.9) IV 29 (20.5) Materials and Methods Nuclear grade G1 14(9.9) G2 41 (28.9) Patients. Surgery was done on 142 patients (median age 57.5 years; G3 86 (60.5) 19-85 years) with early and advanced ovarian carcinoma [International Unknown 1 (0.7) Federation of Gynecology and Obstetrics (FIGO) I-IV] between 1985 Relapsed and 1999 at the Department of Obstetrics and Gynecology, Technical No 73 (51.4) University of Munich. The study was approved by the local ethics Yes 69 (48.6) committee; all patients gave written informed consent. All patients Deceased No 78 (54.9) received postoperative platinum-containing chemotherapy. Median Yes 64(45.1) follow-up in patients still alive at time of analysis was 41 months RT mass (cm) (1-125 months). The collective contains 41 patients (all FIGO IIIc) who 0 72 (50.7) were also included in an earlier report (2). Ascites volume was >0 55 (38.7) estimated preoperatively by ultrasound. Survival and RT statistics are Unknown 15 (10.6) included in Table 1. Ascitic fluid volume Tissue extraction. Ovarian tumor tissue specimens were extracted as No ascites 36 (25.4) V previously described (3); fresh tumor tissue samples were inspected by 500 mL 47 (33.1) the pathologist immediately after surgery, snap-frozen, and stored in >500 mL 48 (33.8) Unknown 11 (7.7) liquid nitrogen. For tissue disintegration, deep-frozen ovarian tissue Response to chemotherapy was pulverized using the microdismembrator II (Braun-Melsungen, Progression 17 (12.0) Melsungen, Germany); the still-frozen powder was then suspended No change 65 (45.8) in extraction buffer (4jC) containing 0.02 mol/L Tris-HCl (pH 8.5), Unknown 60 (42.2) 0.125 mol/L NaCl, and 1% (w/v) nonionic detergent Triton X-100 (15).

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Table 2. Population distribution characteristics of antigen levels for uPA, PAI-1, and tissue kallikreins hK5, hK6, hK7, hK8, hK10, hK11, and hK13 in primary ovarian cancer measured in tissue extracts by ELISA

Antigen n Below detection Range Mean (SD) Percentile (ng/mg protein) limit 25thMedian 75th uPA 141 0 0.02-24.0 1.7 (2.6) 0.4 0.9 1.8 PAI-1 141 1 0.0-389.8 32.8 (54.7) 6.0 14.7 34.4 hK5 142 58 0.0-219.6 3.1 (18.6) 0.0 0.39 1.9 hK6 141 7 0.0-432.7 20.5 (44.2) 1.8 9.4 19.9 hK7 142 31 0.0-283.6 7.8 (26.0) 0.2 2.1 5.2 hK8 142 19 0.0-477.8 28.9 (58.7) 1.4 9.5 24.7 hK10 141 21 0.0-233.6 10.7 (22.4) 1.3 3.9 12.0 hK11 141 33 0.0-155.4 8.3 (21.4) 0.1 1.5 6.2 hK13 141 88 0.0-18.4 0.6 (2.2) 0.0 0.0 0.3

NOTE: The number of measurements ‘‘below detection limit’’ is also given (not to be confused with ‘‘missing’’ values).

In view of their long-tailed distributions (Table 2), all antigen levels Quantitative assessment of uPA, PAI-1, and tissue kallikreins. (uPA, PAI-1, and the tissue kallikreins) were coded as fractional Table 2 summarizes the antigen distributions for uPA, PAI-1, population ranks. The fractional rank difference between PAI-1 and uPA and the seven tissue kallikreins (hK5, hK6, hK7, hK8, hK10, was also coded and considered as a separate indicator. Mann-Whitney hK11, hK13). All are long-tailed, as illustrated by the or Kruskal-Wallis tests were computed for associations between mean, median, and selected percentile values. Table 2 also continuous and categorical variables. The effects of the different factors on PFS and OS were expressed as hazard ratios (HR) with respect to shows the number of patients for which each tissue kallikrein the above coding and were estimated by Cox proportional hazards was not present above the limits of detection. In all of the tissue regression using forward selection. Univariate and multivariate Cox extracts examined, uPA antigen was detected; PAI-1 was models were estimated including clinical factors and fractionally ranked detected in all but one sample. antigen levels, entered as continuous variables. Kaplan-Meier curves There is no significant difference in any antigen levels and log-rank statistics were also computed. between early (FIGO I/II) or advanced (FIGO III/IV) stages Effect of factors on RT was estimated by univariate/multivariate except for hK5 (P = 0.007); higher fractionally ranked hK5 binary logistic regression and expressed as unadjusted/adjusted odds predicts FIGO stage III/IV versus I/II with OR 7.75 [95% ratios (OR), respectively. For each patient, a score (estimated confidence interval (95% CI), 1.7-35.5]. Similarly, of the probability of RT) was obtained and used to construct a receiver antigens, only hK5 was significantly associated (P = 0.004) operating characteristic (ROC; sensitivity / specificity). The area under ROCprovides an in-sample measure of model quality. Analyses were with nuclear grade (higher hK5 implying a tendency to poorer done using SPSS; P values <0.05 were reported as significant. grade). Progression-free survival. Among the clinical variables en- tered into Cox models for PFS in all patients (Table 3), Results univariate predictors were RT, ascites volume, nodal status, FIGO III/IV versus I/II, and FIGO IV versus I/II/III. Among the The present investigation is a retrospective study of the proteolytic factors, hK11 had borderline significance, with high cohort with respect to clinical and proteolytic factors. Table 1 values being favorable (HR, 0.44; 95% CI, 0.19-1.02; P = summarizes clinical and histomorphologic characteristics. 0.055). It was significant in G2/G3 patients (HR, 0.40; 95% CI,

Table 3. Univariate and multivariate Cox proportional hazard regression analyses for PFS in all patients (N=142)

Variable Univariate Multivariate HR (95% CI) P HR (95% CI) P RT presence 4.37 (2.55-7.47) <0.001 4.53 (2.59-7.95) <0.001 FIGO III/IV vs. I/II 9.25 (2.25-38.1) 0.002 — NS FIGO IV vs. I/II/III 2.18 (1.25-3.82) 0.006 — NS Ascites volume >500 mL 2.37 (1.45-3.88) 0.001 — NS Nodal status 1.87 (1.11-3.17) 0.019 — NS Fractionally ranked PAI-1 À fractionally rank uPA 1.98 (1.01-3.89) 0.046 — NS Fractionally ranked hK11 0.44 (0.19-1.02) 0.055 0.30 (0.11-0.76) 0.011

NOTE: Variables entered were FIGO (I-IV), RT presence, ascites volume, age, nuclear grade, nodal status, fractionally ranked antigen levels uPA, PAI-1, and the difference of their fractional ranks, hK5, hK6, hK7, hK8, hK10, hK11, and hK13. All were coded as described in Statistical Methods. Fractionally ranked hK11 was significant in the multivariate model, although only borderline significant in univariate analysis. Although FIGO variables are strong univariate survival factors, they are not significant in the multivariate model (see text). Abbreviation: NS, not significant.

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Table 4. Univariate and multivariate Cox proportional hazard regression analyses for OS in all patients

Variable Univariate Multivariate HR (95% CI) P HR (95% CI) P RT presence 8.23 (4.20-16.0) <0.001 7.49 (3.63-15.45) <0.001 FIGO III/IV vs. I/II 6.35 (1.96-20.6) 0.002 — NS FIGO IV vs. I/II/III 3.32 (1.94-5.67) <0.001 — NS Ascites volume >500 mL 3.52 (2.12-5.87) <0.001 1.97 (1.12-3.46) 0.018 Nodal status 3.73 (1.98-7.01) <0.001 — NS Nuclear grade 2.07 (1.19-3.62) 0.010 — NS Fractionally ranked hK10 0.41 (0.17-0.99) 0.048 0.196 (0.072-0.54) 0.002 Fractionally ranked hK13 0.27 (0.08-0.85) 0.026 — NS

NOTE: Variables entered were FIGO (I-IV), RT presence, ascites volume, age, nuclear grade, nodal status, and fractionally ranked antigen levels uPA, PAI-1, hK5, 6, 7, 8, 10, 11 and 13, coded as described in Statistical Methods. HR for antigens refers to fractional ranks. Although FIGO variables are strong univariate survival factors, they are not significant in the multivariate model.

0.17-0.96; P = 0.039, not shown). The fractional rank difference Discussion between PAI-1 and uPA had significant univariate effect (HR, 1.98; 95% CI, 1.01-3.89; P = 0.046) on PFS. This difference was Elevated serum or tissue levels of hKs have been individually also significant and a bit stronger in the subgroup of patients implicated as diagnostic and prognostic factors in ovarian with RT (HR, 2.86; 95% CI, 1.19-6.84; P = 0.018, not shown) cancer (24). To enhance their usefulness for clinical decisions, but not in the optimally debulked subgroup. this article has evaluated the multivariate effect of seven hKs In multivariate analysis of PFS in all patients (Table 3), RT (hK5, hK6, hK7, hK8, hK10, hK11, and hK13), uPA, and its had HR >4.5; neither FIGO status nor the other clinical inhibitor PAI-1, as well as clinical and histomorphologic variables entered the model, whereas of the antigens hK11 variables on survival and surgical success, in a collective of alone was significant. To illustrate HR 0.30 for fractional ranks, 142 FIGO stage I to IV ovarian cancer patients. consider two hypothetical patients with equal RT but hK11 In both univariate and multivariate analysis, RT was a levels of 6.2 ng/mg (75th percentile) and 0.1 ng/mg (25th decisive (unfavorable) clinical determinant of PFS and OS, in percentile), respectively. Because these fractional ranks differ by accordance with a meta-analysis (25). 0.5, their HR would be (0.30)0.5  0.55. In univariate analysis (PFS), aside from RT, ascites volume Overall survival. For OS in all patients (Table 4), univariate and nodal status were significant clinical variables; the predictors were RT, FIGO stages nodal status, ascites volume, fractional rank difference of PAI-1 and uPA (i.e., high PAI-1 nuclear grade, as well as hK10 and hK13. The clinical factors, RT compared with uPA) was significant for poor PFS; hK11 was of (HR, 7.49) and ascites volume (HR, 1.97), but not FIGO stage, as borderline significance; none of the other proteolytic factors well as antigen hK10 (HR, 0.196) enter multivariate OS model. was significant. In multivariate analysis (PFS), hK11 was the RT presence. Due to the strong effect of surgical success on only statistically significant biomarker, taking RT into account. PFS and OS, any factor influencing surgical success could affect Elevated hK11 tumor antigen levels were associated with survival, independent of its significance in multivariate PFS prolonged PFS. and OS models. Clinical factors ascites volume, age, nuclear In previous studies, the effect of hK11 in ovarian cancer grade, and nodal status, as well as fractionally ranked antigens, depended on whether protein or mRNA levels were considered: were entered into univariate and multivariate logistic regres- KLK11 mRNA expression levels were associated with poor sion for RT (Table 5). Ascites volume (OR, 8.92), nuclear survival in ovarian cancer (26). However, in agreement with grade (OR, 3.01), and hK5 (HR, 1.52) were statistically our findings, elevated tissue hK11 antigen levels determined by significant univariate factors. In multivariate models excluding a similar ELISA were favorable for PFS (14). clinical factors but including the proteolytic factors, higher Our group previously found that uPA and PAI-1 are hK5 (OR, 6.40) and lower hK13 (OR, 0.16) were associated significant prognostic factors regarding OS in FIGO IIICovarian with unfavorable surgical outcome. Entering the relevant cancer. Although uPA was univariately significant, its effect was clinical and proteolytic factors, one obtains the multivariate not seen in multivariate analysis; the effect of PAI-1 was time scoring model for RT on the right of Table 5. Of the clinical varying and more pronounced within the first 2 years (2). factors, larger ascites volume (OR, 13.13) and poorer nuclear In the present collective, high PAI-1 compared with uPA was grade (OR, 2.92) were associated with poorer surgical significantly associated with poor PFS, particularly in the outcome; regarding the antigens, higher hK6 (OR, 8.54) and subgroup of patients with RT. These results taken together lower hK13 (OR, 0.14) contributed to poorer surgical suggest that PAI-1 and uPA could play important but distinct outcome. The model provides risk assessment for RT based roles in the complex process of tumor invasion. Additionally on ascites volume, nuclear grade, hK6, and hK13, which could (aside from the 41 patients in common), the patients in the be estimated before surgery. Figure 1 shows the ROC(sensi- present collective were treated more recently, in particular tivity, specificity for RT) of the probability score computed applying a revised chemotherapy standard (carboplatin/pacli- from this logistic regression model; its area under ROCof taxel as opposed to the earlier carboplatin/cyclophosphamide). 0.833 illustrates the in-sample modeling quality. If uPA and/or PAI-1 were associated with response to

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Table 5. Logistic regression analyses for presence of RT mass

Variable Univariate Multivariate (antigens only) Multivariate withclinical OR (95% CI) P OR (95% CI) P OR (95% CI) P Ascites volume >500 mL 8.92 (3.67-21.7) <0.001 Not entered 13.13 (4.50-38.2) <0.001 Nuclear grade 3.01 (1.40-6.40) 0.005 Not entered 2.92 (1.13-7.5) 0.027 Fractionally ranked hK5 1.52 (1.25-16.6) 0.021 6.40 (1.63-25.0) 0.008 — NS Fractionally ranked hK6 — NS — NS 8.54 (1.57-46.4) 0.013 Fractionally ranked hK13 — NS 0.16 (0.03-0.74) 0.019 0.14 (0.02-0.93) 0.042

NOTE: Factors entered in analyses ‘‘univariate’’ and ‘‘multivariate with clinical’’ were clinical variables ascites volume, age, and nuclear grade, as well as fractionally ranked antigens uPA, PAI-1, and the tissue kallikreins. Variables entered in analysis ‘‘multivariate (antigens only)’’ were fractionally ranked antigens uPA, PAI-1, and the tissue kallikreins. In univariate analysis, OR is unadjusted, whereas in multivariate analyses, OR is adjusted. ORs refer to the coding as described in Statistical Methods; in particular, OR of hK5, hK6, and hK13 refer to fractional ranks. All variables entering models could be estimated before surgery.

carboplatin/paclitaxel therapy, this ‘‘predictive’’ association key determinant of outcome, preoperative determination of could have modified their apparent ‘‘prognostic’’ significance. factors influencing surgical success may be helpful in individ- This hypothesis, although not previously discussed with respect ualizing treatment of ovarian cancer, for example, selection of to uPA and PAI-1 in ovarian cancer, is reminiscent of the patients for second surgical approach after incomplete primary situation in breast cancer (27). surgery or for administration of preoperative chemotherapy. In multivariate analysis, low hK10 levels, RT, and ascites Despite recent improvements, ovarian cancer mortality volume were independently associated with poor OS. It is remains high. FIGO reports 5-year survival of 28.9% for FIGO noteworthy that although high hK13 was a good univariate IIICand 13.4% for FIGO IV (31). predictor of improved OS, it was not significant in the Survival in advanced ovarian cancer is better for patients multivariate model upon inclusion of RT; hence, the univariate without macroscopic RT after primary surgery compared with effect may be partly attributable to its ability to predict absence patients with RT (25, 32, 33). However, radical tumor resection of RT mass. In contrast, elevated hK10 is an even stronger may cause considerable morbidity (e.g., postoperative subileus, predictor of favorable OS in a multivariate model that includes relaparotomy due to anastomosis defects, cardiorespiratory RT than in univariate analysis of OS. These findings seem failure, thromboembolism, sepsis, or lymphoceles). Systematic consistent with hK10 down-regulation during tumor progres- lymph node dissection—a recommended procedure when sion in nude mice inoculated with breast or prostate cancer cell optimal debulking seems achievable—by itself often causes lines (28, 29). significantly higher blood loss and consecutive transfusion rates Unfavorable outcome in FIGO III/IV ovarian cancer was compared with surgery without radical lymphadenectomy (34). reported to be associated with hK10 tissue levels (30) exceeding Our overall morbidity rate after advanced ovarian cancer a cutoff; however, hK10 was neither significant as a continuous variable nor in multivariate analysis including all FIGO I-IV patients. In view of the still unresolved underlying biological complexity, a nonmonotonic relationship between hK10 and tumor aggressiveness cannot be ruled out. The partly discrepant results in the literature indicate that the existing data on the diagnostic and prognostic role of hKs in ovarian cancer merit further investigation. Differences in patient selection, treat- ment, determination methods, and use of optimal cutoffs could be relevant. Because chemotherapy (platinum compounds F taxane) was rather homogeneously administered to our primary ovarian cancer patients, the observed effect of tumor biological factors on survival (PFS, OS) reflects a superposition of their biological role in the natural course of disease and their influence on therapy response: The observed survival effect of a factor could be modified or masked by a predictive component with respect to therapy response. However, because ovarian cancer spread- ing is primarily locoregional, factors for tumor aggressiveness are reflected not only in survival but also already in the tumor burden within the abdominal cavity. Hence, studying relation- ships between tumor-associated factors and probability of surgical success reveals key information on localized tumor Fig. 1. ROC for prediction of RT, based on the model of Table 5 and including aggressiveness free of confounding effects of therapy response. ascites volume, nuclear grade, and tissue kallikreins hK6 and hK13. Area under the Moreover, because completeness of cytoreductive surgery is a ROC curve is 0.833. Note that this is an in-sample estimate.

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surgery is f40%, but radical surgery may lead to up to 80% indicate an alternative therapeutic approach, such as preoper- morbidity (35). High morbidity is certainly acceptable if ative chemotherapy. Up to now, preoperative chemotherapy is optimal debulking with its attendant survival advantage can not a standard of care in the management of ovarian cancer. be achieved; however, it must be viewed critically for patients in But recruitment of the European Organization for Research whom optimal debulking was not achievable and who thus and Treatment of Cancer protocol 55971 comparing upfront have a short remaining life expectancy. debulking surgery versus neoadjuvant chemotherapy in To estimate risks and benefits of surgical interventions patients with stage IIIc or IV epithelial ovarian cancer in a preoperatively, clinical or biological factors are urgently needed randomized phase III trial is almost completed. This trial will that could more accurately predict individual debulking elucidate whether neoadjuvant chemotherapy is an alternative success. In ovarian cancer, tumor biological processes may approach with the same outcome compared with the standard strongly affect the degree of diffusion and dissemination of procedure, upfront surgery (36, 37). Thus far, no reliable tumor cells within the abdominal cavity and hence influence parameters exist to predict which patients are likely to benefit the chance for surgical success. Indeed, in our study, besides from preoperative chemotherapy. In a pilot study, we showed large ascites volume and high nuclear grade, high hK5 that patients with large ascites volume who received preoper- univariately predicted poor surgical success. Moreover, in ative chemotherapy followed by radical surgery had less RT at multivariate analysis, besides favorable grade and low ascites time of definitive surgery and a substantial survival advantage volume, low hK6 and high hK13 predicted surgical success. We over comparable patients receiving chemotherapy after surgery previously described the predictive effect of preoperative ascites (38). A predictive score combining tumor biological with for surgical outcome in FIGO IIIc ovarian cancer (2). Regarding clinical information—in our case using tissue kallikreins hK6, hKs, there is limited information regarding their effect on hK13, ascites volume, and nuclear grade—may thus help to surgical success: For elevated hK10, a significant association identify suitable candidates for preoperative chemotherapy. with large RT was reported (30); for hK11, a significant It would be useful to test the validity of the predictive score by correlation with response to chemotherapy, but not with prospective measurement of these factors in a study using surgical outcome was observed (14). current treatment guidelines. With a validated scoring model Using a logistic regression model, we have now obtained a for surgical success in ovarian cancer, the next step would then score (RT score) based on hK6, hK13, ascites volume, and be to conduct a clinical trial designed to test whether patients nuclear grade that predicted surgical outcome with good benefit from treatment strategies that incorporate the new performance (area under ROC, 0.833). Such a score could be scoring information. calculated before definitive surgery: preoperative ascitic fluid In conclusion, our study shows that some of the hKs are volume is easily estimated by ultrasound; tumor biopsies can significant independent prognostic and predictive markers in be obtained for tissue analysis. The score could support ovarian cancer. Their role in disease progression is evidently preoperative risk stratification: In patients with considerable complex, as different tissue kallikreins have a strong effect comorbidity, a favorable RT score would reinforce the decision for different disease end points. Nevertheless, they have the for radical surgery, whereas an unfavorable RT score might potential to support clinical therapy decisions.

References 1. van der Burg M, Henzen-Logmans SC, Berns E, van diagnosis and prognosis of ovarian carcinoma. J Clin (uPA) and PAI-1predict survival in advanced ovarian Putten WL, Klijn JG, Foekens JA. Expression of uro- Oncol 2003;21:1035 ^ 43. cancer patients (FIGO III) after radical surgery and kinase-type plasminogen activator (uPA) and its 9. Luo LY, Katsaros D, Scorilas A, et al. The serum con- platinum-based chemotherapy. Gynecol Oncol 1994; inhibitor PAI-1 in benign, borderline, malignant primary centration of human kallikrein 10 represents a novel 55:401 ^ 9. and metastatic ovarian tumors. Int J Cancer 1996; biomarker for ovarian cancer diagnosis and prognosis. 17. ChristopoulosTK, Diamandis EP. Enzymatically am- 69:475 ^9. Cancer Res 2003;63:807 ^ 11. plified time-resolved fluorescence immunoassay with 2. Kuhn W, Schmalfeldt B, Reuning U, et al. Prognostic 10. Diamandis EP, Borgono CA, Scorilas A, et al. Immu- terbium chelates. Anal Chem 1992;64:342 ^ 6. significance of urokinase (uPA) and its inhibitor PAI-1 nofluorometric quantification of human kallikrein 5 18. Yousef GM, Polymeris ME, Grass L, et al. Human for survival in advanced ovarian carcinoma stage FIGO expression in ovarian cancer cytosols and its associa- kallikrein 5: a potential novel serum biomarker for IIIc. BrJ Cancer 1999;79:1746^51. tion with unfavorable patient prognosis. Tumour Biol breast and ovarian cancer. Cancer Res 2003;63: 3. SchmalfeldtB,PrechtelD,HartingK,etal.Increased 2003;24:299 ^ 309. 3958^65. expression of matrix metalloproteinases (MMP)-2, 11. KishiT, Grass L, Soosaipillai A, et al. Human kallikrein 19. DiamandisEP,YousefGM,SoosaipillaiAR,Bunting MMP-9, and the urokinase-type plasminogen activa- 8, a novel biomarker for ovarian carcinoma. Cancer P. Human kallikrein 6 (zyme/protease M/neurosin): tor is associated with progression from benign to ad- Res 2003;63:2771 ^ 4. a new serum biomarker of ovarian carcinoma. Clin vanced ovarian cancer. Clin Cancer Res 2001;7: 12. Diamandis EP, Borgono CA, Scorilas A, Harbeck N, Biochem 2000;33:579 ^ 83. 2396^404. Dorn J, Schmitt M. Human kallikrein 11: an indicator of 20. Kishi T, Soosaipillai A, Grass L, Little SP, Johnstone 4. Konecny G, Untch M, Pihan A, et al. Association of favorable prognosis in ovarian cancer patients. Clin EM, Diamandis EP. Development of an immunofluoro- urokinase-type plasminogen activator and its inhibitor Biochem 2004;37:823^ 9. metric assay and quantification of human kallikrein 7 with disease progression and prognosis in ovarian 13 . Scorilas A, Borgono CA, Harbeck N, et al. Human in tissue extracts and biological fluids. Clin Chem cancer. Clin Cancer Res 2001;7:1743^ 9. kallikrein 13 protein in ovarian cancer cytosols: a new 2004;50:709 ^ 16. 5. Borgono CA, Michael IP, Diamandis EP. Human tis- favorable prognostic marker. J Clin Oncol 2004;22: 21. Kishi T, Grass L, Soosaipillai A, Shimizu-Okabe C, sue kallikreins: physiologic roles and applications in 678 ^ 85. Diamandis EP. Human kallikrein 8: immunoassay cancer. Mol Cancer Res 2004;2:257 ^ 80. 14. Borgono CA, Fracchioli S, Yousef GM, et al. Favor- development and identification in tissue extracts and 6. Yousef GM, Polymeris ME,Yacoub GM, et al. Parallel able prognostic value of tissue human kallikrein 11 biological fluids. Clin Chem 2003;49:87 ^ 96. overexpression of seven kallikrein genes in ovarian (hK11)in patients with ovarian carcinoma. IntJ Cancer 22. Luo LY, Grass L, Howarth DJ, Thibault P, Ong H, cancer. Cancer Res 2003;63:2223 ^ 7. 2003;106:605^10. Diamandis EP. Immunofluorometric assay of human 7. Rosen DG, Wang L, Atkinson JN, et al. Potential 15. Schmitt M, Sturmheit AS, Welk A, Schnelldorfer C, kallikrein 10 and its identification in biological fluids markers that complement expression of CA125 in Harbeck N. Procedures for the quantitative protein and tissues. Clin Chem 2001;47:237^ 46. epithelial ovarian cancer. Gynecol Oncol 2005;99: determination of urokinase and its inhibitor, PAI-1, in 23. Kapadia C, Chang A, Sotiropoulou G, et al. 267 ^ 77. human breast cancer tissue extracts by ELISA. Human kallikrein 13: production and purification of 8. Diamandis EP, Scorilas A, Fracchioli S, et al. Human Methods Mol Med 2006;120:245^ 65. recombinant protein and monoclonal and polyclonal kallikrein 6 (hK6): a new potential serum biomarker for 16. Kuhn W, Pache L, Schmalfeldt B, et al. Urokinase antibodies, and development of a sensitive and

www.aacrjournals.org 1747 Clin Cancer Res 2007;13(6) March15, 2007 Downloaded from clincancerres.aacrjournals.org on September 28, 2021. © 2007 American Association for Cancer Research. Imaging, Diagnosis, Prognosis

specific immunofluorometric assay. Clin Chem 2003; sion of which is down-regulated during breast cancer 34. Benedetti-Panici P, Maggioni A, Hacker N, et al. 49:77^86. progression. Cancer Res 1996;56:3371 ^ 9. Systematic aortic and pelvic lymphadenectomy versus 24. Borgono CA, Diamandis EP. The emerging roles of 29. Goyal J, Smith KM, Cowan JM, Wazer DE, Lee resection of bulky nodes only in optimally debulked human tissue kallikreins in cancer. Nat Rev Cancer SW, Band V. The role for NES1 serine protease as a advanced ovarian cancer: a randomized clinical trial. 2004;4:876 ^ 90. novel tumor suppressor. Cancer Res 1998;58: J Natl Cancer Inst 2005;97:560 ^ 6. 25. Bristow RE, Tomacruz RS, Armstrong DK, Trimble 4782 ^ 6. 35. Eisenkop SM, Friedman RL, Wang HJ. Complete EL, Montz FJ. Survival effect of maximal cytoreduc- 30. Luo LY, Katsaros D, Scorilas A, et al. Prognos- cytoreductive surgery is feasible and maximizes sur- tive surgery for advanced ovarian carcinoma during tic value of human kallikrein 10 expression in epi- vival in patients with advanced epithelial ovarian can- the platinum era: a meta-analysis. J Clin Oncol 2002; thelial ovarian carcinoma. Clin Cancer Res 2001;7: cer: a prospective study. Gynecol Oncol 1998;69: 20:1248 ^59. 2372 ^ 9. 103 ^ 8. 26. Shigemasa K, Gu L, Tanimoto H, O’Brien TJ, Ohama 31. Heintz APM, Odicino F, Maisonneuve P, et al. Carci- 36.Vergote I, van GorpT, Amant F, Neven P,Berteloot P. K. Human kallikrein gene 11 (KLK11) mRNA overex- noma of the ovary. In: FIGO annual report on the Neoadjuvant chemotherapy for ovarian cancer. Onco- pression is associated with poor prognosis in patients results of treatment in gynecological cancer. Int J logy (Huntingt) 2005;19:1615^ 22. with epithelial ovarian cancer. Clin Cancer Res 2004; Gynaecol Obstet 2002;83 Suppl 1:135 ^ 66. 37. van Gorp T, Amant F, Neven P, Berteloot P, Leunen 10:2766 ^ 70. 32. du Bois A, Luck HJ, Meier W, et al. A randomized K,Vergote I.The position of neoadjuvant chemothera- 27. Harbeck N, Kates R, Look M, et al. Enhanced bene- clinical trial of cisplatin/paclitaxel versus carboplatin/ py within the treatment of ovarian cancer. Minerva fit from adjuvant chemotherapy in breast cancer paclitaxel as first-line treatment of ovarian cancer. J Gynecol 2006;58:393 ^ 403. patients classified high-risk according to urokinase- Natl Cancer Inst 2003;95:1320^9. 38. Kuhn W, Rutke S, Spathe K, Schmalfeldt B, et al. type plasminogen activator (uPA) and plasminogen 33. Hoskins WJ, McGuire WP, Brady MF, et al. The ef- Neoadjuvant chemotherapy followed by tumor activator inhibitor type 1 (n = 3424). Cancer Res fect of diameter of largest residual disease on survival debulking prolongs survival for patients with poor 2002;62:4617^ 22. after primary cytoreductive surgery in patients with prognosis in International Federation of Gynecology 28. Liu XL, Wazer DE,Watanabe K, Band V. Identifica- suboptimal residual epithelial ovarian carcinoma. Am and Obstetrics Stage IIIC ovarian carcinoma. Cancer tion of a novel serine protease-like gene, the expres- J Obstet Gynecol 1994;170:974 ^ 9. 2001;92:2585 ^ 91.

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Julia Dorn, Manfred Schmitt, Ronald Kates, et al.

Clin Cancer Res 2007;13:1742-1748.

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